Contributions:
We propose a ZSLDG approach comprising of
a visual encoder that learns to project multi-domain images from the visual space to a latent space, and a semantic encoder that learns to map text-based category-specific semantic representations to the same latent space.
The key contributions of the proposed approach are:
(i) For aligning class-specific cues from visual and semantic latent embeddings, we introduce a multimodal alignment loss term;
(ii) We propose to partition the latent space w.r.t. class-level semantic concepts across domains by minimizing intra-class variance across different seen domains; (iii) The focus of our design is introduction of a joint invariance module that seeks to achieve domain invariance w.r.t. the visual-semantic joint distribution, and thereby facilitates generalizing to unseen classes in unseen domains; and (iv) Experiments and ablation studies on the challenging DomainNet and DomainNet-LS benchmarks [1]} demonstrate the superiority of our approach over existing methods. Particularly, on the most difficult quickdraw domain, our approach achieves a significant gain of \(1.6\%\)  over the best existing method [2]}.
